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End-to-End Differentiable Diffusion Conditioning for Physics-Informed Optimization
Not provided in the abstract
optimizationdifferentiable programmingphysics-informedmachine learning
2610.31721
Builder Relevance
1h ago80%
Abstract
The paper proposes a novel method for optimizing high-dimensional physical systems using a differentiable diffusion model.
Reality Card
Core Claim
The proposed Differentiable Diffusion Conditioning (D2C) method transforms a frozen conditional diffusion model into an end-to-end gradient-based optimizer, achieving superior results in real-world tasks.
Method / Result
Achieved the strongest results on two real-world tasks and the best average rank across four Design-Bench tasks.
Limitations
The method relies on a fixed dataset and may face challenges in generalizing to new test-time conditions.
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